Detection and analysis of deterioration trends in construction machines operation

Abstract

The paper proposes a computational strategy for discovering trends of changes and gradual performance deterioration of construction machines and other complex systems. If a significant change in the machine performance is detected, it further activates a fault diagnosis procedure in order to find the possible faulty condition. In order to send the information from various sensors of the construction machine to the maintenance center in an efficient form, a special information compression method is proposed in the paper. It uses original unsupervised learning algorithm to locate the given number of neurons from the parameter space in the densest data areas. These neurons are considered as information granules, which are later on sent in a wireless way to the maintenance center to represent the current machine operation. Two information recovery procedures are also proposed in the paper for analyzing the compressed information from the neurons. The first one is a specialized version of the moving window method, while the other is fuzzy inference-based approach for discovering possible machine deterioration. Results based on real experimental data from a hydraulic excavator are used to explain the proposed computational strategy and its merits.